Understanding how local weights swap aggressively into unified memory architectures during concurrent GPU strain.
When rendering multiple local neural networks, consumer VRAM sizes (typically 8GB to 24GB) are rapidly exhausted. The traditional solution is Out-Of-Memory (OOM) crashes.
WebGPU allows seamless pipeline integration where excess attention layers are preemptively swapped to localized System RAM (or unified memory on Apple Silicon). While this penalizes TPS dynamically, it guarantees crash-free parallel inferences without dropping contexts.